At The End Of Happy Hour!

Project 1

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Team members

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• Anthony Garcia
• Katrina Fletcher
• Nancy Campos
• Scott Dutton

PREMISE

Our group wanted to find the correlation of alcohol consumption in comparison to happiness worldwide.  Based
on data gathered from 112 countries, we were able to determine the top five "happiest" countries and the 
bottom 5 least "happiest" countries.

The happiness score is based on a scale from 0 to 10 with 10 being the happiest.

import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import os
import numpy as np
from scipy.stats import linregress
import gmaps
import plotly.express as px

happiness_path = os.path.join("Resources", "HappinessAlcoholConsumption.csv")
income_path = os.path.join("Resources", "countries_income_group.csv")
country_path = os.path.join("Resources", "world_country_and_usa_states_latitude_and_longitude_values.csv")

happiness_study_df = pd.read_csv(happiness_path)
income_study_df = pd.read_csv(income_path, index_col=[0])
df_lat = pd.read_csv(country_path) 

df_lat = df_lat[['latitude','longitude','country']]
df_lat.columns = ['latitude','longitude','Country']
df_lat = pd.merge(happiness_study_df,df_lat,on='Country')

income_study_df.rename(columns = {'Economy' : 'Country'}, inplace = True)

alcohol_study = pd.merge(happiness_study_df, income_study_df, on = "Country")

final_alcohol = alcohol_study.drop(columns=['Code', 'Region_y'])
final_alcohol

	Country	Region_x	Hemisphere	HappinessScore	HDI	GDP_PerCapita	Beer_PerCapita	Spirit_PerCapita	Wine_PerCapita	Income group
0	Denmark	Western Europe	north	7.526	928	53.579	224	81	278	High income
1	Switzerland	Western Europe	north	7.509	943	79.866	185	100	280	High income
2	Iceland	Western Europe	north	7.501	933	60.530	233	61	78	High income
3	Norway	Western Europe	north	7.498	951	70.890	169	71	129	High income
4	Finland	Western Europe	north	7.413	918	43.433	263	133	97	High income
...	...	...	...	...	...	...	...	...	...	...
107	Madagascar	Sub-Saharan Africa	south	3.695	517	402.000	26	15	4	Low income
108	Tanzania	Sub-Saharan Africa	south	3.666	533	878.000	36	6	1	Low income
109	Liberia	Sub-Saharan Africa	north	3.622	432	455.000	19	152	2	Low income
110	Benin	Sub-Saharan Africa	north	3.484	512	789.000	34	4	13	Low income
111	Togo	Sub-Saharan Africa	north	3.303	500	577.000	36	2	19	Low income

112 rows × 10 columns

# Markdown #1 - What Are the top 5 Happiest Countries

Question to answer

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What Are the top 5 Happiest Countries?

#Filter to find top5 happiest countries 
top5 = final_alcohol.loc[final_alcohol['HappinessScore'] > 7.41]
top5

	Country	Region_x	Hemisphere	HappinessScore	HDI	GDP_PerCapita	Beer_PerCapita	Spirit_PerCapita	Wine_PerCapita	Income group
0	Denmark	Western Europe	north	7.526	928	53.579	224	81	278	High income
1	Switzerland	Western Europe	north	7.509	943	79.866	185	100	280	High income
2	Iceland	Western Europe	north	7.501	933	60.530	233	61	78	High income
3	Norway	Western Europe	north	7.498	951	70.890	169	71	129	High income
4	Finland	Western Europe	north	7.413	918	43.433	263	133	97	High income

#variables for bar graph
x = top5['Country']
y = top5['HappinessScore']

color = ['#1053e3', '#1072e3', '#1092e3','#10abe3','#10bce3']

#plot the bar graph
plt.bar(x,y, color=color)

#add labels and styling to graph
plt.xticks(fontsize=15, fontweight='bold')
plt.xlabel('Countries', fontsize=15, labelpad=35)
plt.ylabel('Happiness Score', fontsize=15, labelpad=35)
plt.title('Top Five Happiest Countries', fontsize=25, loc='center', fontweight='bold')
plt.ylim(7.35,7.55)
fig = plt.gcf()
fig.set_size_inches(20,10)

plt.show()

# Markdown #2 - What are the bottom 5 happiest countries?

Question to answer

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What are the bottom 5 happiest countries?

#Filter to find bottom5 least happiest countries
bottom5 = final_alcohol.loc[final_alcohol['HappinessScore'] <= 3.7]
sortbottom= bottom5.sort_values('HappinessScore', ascending=True)

#variables for graph
x = sortbottom['Country']
y = sortbottom['HappinessScore']
color = ['#E3504D', '#E3754D', '#E39B4D','#E3B94D','#E3D74D']
#plot the graph
plt.bar(x,y, color=color)
#labeling and stying for graph
plt.xticks(fontsize=15, fontweight='bold')
plt.xlabel('Countries', fontsize=15, labelpad=35)
plt.ylabel('Happiness Score', fontsize=15, labelpad=35)
plt.title('Bottom Five Least Happiest Countries',fontsize=25, loc='center', fontweight='bold')
plt.ylim(3.0,3.8)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

# Markdown #3 - Which Alcohol is the most popular?

Question to answer

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Which Alcohol is the most popular?

beer = final_alcohol['Beer_PerCapita'].mean()
spirit = final_alcohol['Spirit_PerCapita'].mean()
wine = final_alcohol['Wine_PerCapita'].mean()

best_alcohol = pd.DataFrame({"Average Beer Per Capita" : [beer],
                            "Average Spirit Per Capita" : spirit,
                            "Average Wine Per Capita" : wine})

best_alcohol.head()

	Average Beer Per Capita	Average Spirit Per Capita	Average Wine Per Capita
0	141.258929	100.071429	70.276786

alcohol = ["Beer", "Spirit", "Wine"]
avg_pc = [141.26, 100.07, 70.28]
x_axis = np.arange(len(avg_pc))

plt.bar(x_axis, avg_pc, color = "g", alpha = .8, align= 'center')
plt.xticks(x_axis, alcohol,fontsize=15, fontweight='bold')
plt.title("Beer vs Wine vs Spirit per Capita",fontsize=25, loc='center', fontweight='bold')
plt.xlabel("Alcohol",fontsize=15, labelpad=35)
plt.ylabel("Average Alcohol Type Per Capita",fontsize=15, labelpad=35)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

# Markdown #4 - For the top 5, what is their alcohol of choice?

Question to answer

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For the top 5, what is their alcohol of choice?

top5 = final_alcohol.loc[final_alcohol['HappinessScore']>7.41]

x_axis = np.arange(len(top5['Country']))

plt.bar(x_axis -.10, top5['Beer_PerCapita'], width=0.2, label = 'Beer')
plt.bar(x_axis +.10, top5['Spirit_PerCapita'], width=0.2, label = 'Spirits')
plt.bar(x_axis +.10*3, top5['Wine_PerCapita'], width=0.2, label = 'Wine')
plt.title("Alcohol of Choice for Top Five",fontsize=25, loc='center', fontweight='bold')
plt.xticks(x_axis, top5['Country'],fontsize=15, fontweight='bold')

plt.legend(fontsize=15)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

#Markdown #5 - For bottom 5, what is their alcohol of choice?

Question to answer

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For bottom 5, what is their alcohol of choice?

bottom5 = final_alcohol.loc[final_alcohol['HappinessScore']<=3.7]

x_axis = np.arange(len(bottom5['Country']))

plt.bar(x_axis -.10, bottom5['Beer_PerCapita'], width=0.2, label = 'Beer')
plt.bar(x_axis +.10, bottom5['Spirit_PerCapita'], width=0.2, label = 'Spirits')
plt.bar(x_axis +.10*3, bottom5['Wine_PerCapita'], width=0.2, label = 'Wine')
plt.title("Alcohol of Choice for Bottom Five",fontsize=25, loc='center', fontweight='bold')
plt.xticks(x_axis, bottom5['Country'],fontsize=15, fontweight='bold')

plt.legend(fontsize=15)
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

# Markdown #6 - Is there a relationship between income level and happiness?  Are they highly correlated?

Question to answer

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Is there a relationship between income level and happiness? Are they highly correlated?

x = final_alcohol['Income group']
y= final_alcohol['HappinessScore']

plt.scatter(x,y,s=500, edgecolor='k', facecolors='#32a852')

plt.ylim(3,8)
plt.xticks(fontsize=15, fontweight='bold')
plt.xlabel('Income Level', fontsize=15, labelpad=35)
plt.ylabel('Happiness Score', fontsize=15, labelpad=35)
plt.title('Income vs. Happiness Score',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

# Calculate the correlation coefficient and linear regression model 
# for GDP per Capita and HappinessScore

GDP_per = final_alcohol.iloc[:,5]
Happiness_score = final_alcohol.iloc[:,3]
correlation = st.pearsonr(GDP_per,Happiness_score)
print(f"The correlation between GDP per Capita and Happiness Score is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['HappinessScore']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(5.5,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Happiness Score", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Happiness Score',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

The correlation between GDP per Capita and Happiness Score is -0.49.

#Markdown #6 - is there a relationship between GDP and each alcohol of choice?

Question to answer

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Is there a relationship between GDP and each alcohol of choice?

GDP_per = final_alcohol.iloc[:,5]
Beer_score = final_alcohol.iloc[:,6]
correlation = st.pearsonr(GDP_per,Beer_score)
print(f"The correlation between GDP per Capita and Beer Per Capita is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['Beer_PerCapita']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(200.0,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Beer Per Capita", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Beer Per Capita',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

The correlation between GDP per Capita and Beer Per Capita is -0.4.

GDP_per = final_alcohol.iloc[:,5]
Wine_score = final_alcohol.iloc[:,8]
correlation = st.pearsonr(GDP_per,Wine_score)
print(f"The correlation between GDP per Capita and Wine Per Capita is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['Wine_PerCapita']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(200.0,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Wine Per Capita", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Wine Per Capita',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

The correlation between GDP per Capita and Wine Per Capita is -0.21.

GDP_per = final_alcohol.iloc[:,5]
Spirit_score = final_alcohol.iloc[:,7]
correlation = st.pearsonr(GDP_per,Spirit_score)
print(f"The correlation between GDP per Capita and Spirit Per Capita is {round(correlation[0],2)}.")


# Calculate linear regression
x_values = final_alcohol['Spirit_PerCapita']
y_values = final_alcohol['GDP_PerCapita']
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept,2))
regress_values = x_values * slope + intercept

plt.scatter(x_values,y_values)
plt.plot(x_values,regress_values,"r-")
plt.annotate(line_eq,(200.0,200.0),fontsize=20,color="red",fontweight='bold')

# Add labels and title to plot
plt.xlabel("Spirit Per Capita", fontsize=15, labelpad=35)
plt.ylabel("GDP per Capita", fontsize=15, labelpad=35)
plt.title('GDP per Capita vs Spirit Per Capita',fontsize=25, loc='center', fontweight='bold')
fig = plt.gcf()
fig.set_size_inches(20,10)
plt.show()

The correlation between GDP per Capita and Spirit Per Capita is -0.28.

# Markdown #7 - Heat Map of Happiness Scores

Heat Map of Country versus Happiness Scores

fig = px.choropleth(df_lat, 
                    locations="Country", 
                    color="HappinessScore", 
                    locationmode = 'country names', 
                    hover_name="Country",
                    range_color=[0,8],
                    title='Country vs happiness score')
fig.show()